Overview

Brought to you by YData

Dataset statistics

Number of variables14
Number of observations291
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory58.8 KiB
Average record size in memory206.8 B

Variable types

Numeric8
Categorical5
DateTime1

Alerts

mine has constant value "1" Constant
machine has constant value "1" Constant
operator has constant value "1" Constant
distance_travelled is highly overall correlated with engine_hours and 6 other fieldsHigh correlation
engine_hours is highly overall correlated with distance_travelled and 6 other fieldsHigh correlation
fuel_consumed is highly overall correlated with distance_travelled and 6 other fieldsHigh correlation
idle_hours is highly overall correlated with distance_travelled and 6 other fieldsHigh correlation
no_of_loads is highly overall correlated with distance_travelled and 6 other fieldsHigh correlation
operating_hours is highly overall correlated with distance_travelled and 6 other fieldsHigh correlation
tonnage is highly overall correlated with distance_travelled and 6 other fieldsHigh correlation
transmission_hours is highly overall correlated with distance_travelled and 6 other fieldsHigh correlation
id has unique values Unique
distance_travelled has 20 (6.9%) zeros Zeros
tonnage has 79 (27.1%) zeros Zeros
fuel_consumed has 20 (6.9%) zeros Zeros
no_of_loads has 79 (27.1%) zeros Zeros
operating_hours has 54 (18.6%) zeros Zeros
engine_hours has 99 (34.0%) zeros Zeros
transmission_hours has 99 (34.0%) zeros Zeros

Reproduction

Analysis started2025-02-17 09:41:29.706090
Analysis finished2025-02-17 09:41:36.811247
Duration7.11 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

id
Real number (ℝ)

Unique 

Distinct291
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean285.04467
Minimum1
Maximum763
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2025-02-17T11:41:37.128632image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile15.5
Q173.5
median146
Q3635
95-th percentile741.5
Maximum763
Range762
Interquartile range (IQR)561.5

Descriptive statistics

Standard deviation279.43492
Coefficient of variation (CV)0.98031976
Kurtosis-1.2844964
Mean285.04467
Median Absolute Deviation (MAD)91
Skewness0.7450551
Sum82948
Variance78083.877
MonotonicityStrictly increasing
2025-02-17T11:41:37.215071image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
763 1
 
0.3%
1 1
 
0.3%
2 1
 
0.3%
739 1
 
0.3%
738 1
 
0.3%
736 1
 
0.3%
735 1
 
0.3%
733 1
 
0.3%
732 1
 
0.3%
730 1
 
0.3%
Other values (281) 281
96.6%
ValueCountFrequency (%)
1 1
0.3%
2 1
0.3%
3 1
0.3%
4 1
0.3%
5 1
0.3%
6 1
0.3%
7 1
0.3%
8 1
0.3%
9 1
0.3%
10 1
0.3%
ValueCountFrequency (%)
763 1
0.3%
762 1
0.3%
760 1
0.3%
759 1
0.3%
757 1
0.3%
756 1
0.3%
754 1
0.3%
753 1
0.3%
751 1
0.3%
750 1
0.3%

shift_number
Categorical

Distinct2
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size14.3 KiB
1
147 
2
144 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters291
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row2
4th row1
5th row2

Common Values

ValueCountFrequency (%)
1 147
50.5%
2 144
49.5%

Length

2025-02-17T11:41:37.286098image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-17T11:41:37.338094image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 147
50.5%
2 144
49.5%

Most occurring characters

ValueCountFrequency (%)
1 147
50.5%
2 144
49.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 291
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 147
50.5%
2 144
49.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 291
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 147
50.5%
2 144
49.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 291
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 147
50.5%
2 144
49.5%

date
Date

Distinct149
Distinct (%)51.2%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
Minimum2018-12-15 00:00:00
Maximum2024-12-11 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-02-17T11:41:37.418283image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T11:41:37.509284image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

distance_travelled
Real number (ℝ)

High correlation  Zeros 

Distinct21
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.594502
Minimum0
Maximum235
Zeros20
Zeros (%)6.9%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2025-02-17T11:41:37.579234image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median9
Q314
95-th percentile19
Maximum235
Range235
Interquartile range (IQR)11

Descriptive statistics

Standard deviation30.025056
Coefficient of variation (CV)2.3839812
Kurtosis49.96622
Mean12.594502
Median Absolute Deviation (MAD)6
Skewness7.0399236
Sum3665
Variance901.50397
MonotonicityNot monotonic
2025-02-17T11:41:37.629154image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
3 25
 
8.6%
12 23
 
7.9%
1 21
 
7.2%
6 20
 
6.9%
0 20
 
6.9%
15 18
 
6.2%
9 16
 
5.5%
13 16
 
5.5%
2 13
 
4.5%
16 12
 
4.1%
Other values (11) 107
36.8%
ValueCountFrequency (%)
0 20
6.9%
1 21
7.2%
2 13
4.5%
3 25
8.6%
4 11
3.8%
5 9
 
3.1%
6 20
6.9%
7 12
4.1%
8 10
 
3.4%
9 16
5.5%
ValueCountFrequency (%)
235 5
 
1.7%
19 11
3.8%
18 11
3.8%
17 12
4.1%
16 12
4.1%
15 18
6.2%
14 6
 
2.1%
13 16
5.5%
12 23
7.9%
11 10
3.4%

tonnage
Real number (ℝ)

High correlation  Zeros 

Distinct33
Distinct (%)11.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean130.88316
Minimum0
Maximum329
Zeros79
Zeros (%)27.1%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2025-02-17T11:41:37.683223image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median132
Q3222
95-th percentile294
Maximum329
Range329
Interquartile range (IQR)222

Descriptive statistics

Standard deviation102.52129
Coefficient of variation (CV)0.78330386
Kurtosis-1.2433303
Mean130.88316
Median Absolute Deviation (MAD)92
Skewness0.085174626
Sum38087
Variance10510.614
MonotonicityNot monotonic
2025-02-17T11:41:37.748655image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
0 79
27.1%
180 17
 
5.8%
90 12
 
4.1%
222 10
 
3.4%
154 9
 
3.1%
210 9
 
3.1%
224 9
 
3.1%
70 8
 
2.7%
105 8
 
2.7%
119 8
 
2.7%
Other values (23) 122
41.9%
ValueCountFrequency (%)
0 79
27.1%
34 5
 
1.7%
60 3
 
1.0%
70 8
 
2.7%
72 5
 
1.7%
84 4
 
1.4%
90 12
 
4.1%
102 4
 
1.4%
105 8
 
2.7%
119 8
 
2.7%
ValueCountFrequency (%)
329 4
1.4%
315 7
2.4%
294 5
1.7%
282 7
2.4%
280 4
1.4%
270 4
1.4%
259 8
2.7%
252 7
2.4%
245 3
 
1.0%
240 8
2.7%

fuel_consumed
Real number (ℝ)

High correlation  Zeros 

Distinct51
Distinct (%)17.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean79.924399
Minimum0
Maximum200
Zeros20
Zeros (%)6.9%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2025-02-17T11:41:37.811637image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q130
median80
Q3120
95-th percentile171
Maximum200
Range200
Interquartile range (IQR)90

Descriptive statistics

Standard deviation54.662423
Coefficient of variation (CV)0.68392661
Kurtosis-1.0168801
Mean79.924399
Median Absolute Deviation (MAD)48
Skewness0.23267782
Sum23258
Variance2987.9805
MonotonicityNot monotonic
2025-02-17T11:41:37.880730image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 20
 
6.9%
120 16
 
5.5%
90 13
 
4.5%
30 12
 
4.1%
72 9
 
3.1%
60 8
 
2.7%
144 8
 
2.7%
108 8
 
2.7%
160 7
 
2.4%
8 7
 
2.4%
Other values (41) 183
62.9%
ValueCountFrequency (%)
0 20
6.9%
8 7
 
2.4%
9 7
 
2.4%
10 7
 
2.4%
16 3
 
1.0%
18 5
 
1.7%
20 5
 
1.7%
24 7
 
2.4%
27 6
 
2.1%
30 12
4.1%
ValueCountFrequency (%)
200 5
1.7%
190 2
 
0.7%
180 6
2.1%
171 3
 
1.0%
170 1
 
0.3%
160 7
2.4%
153 6
2.1%
152 6
2.1%
150 6
2.1%
144 8
2.7%

no_of_loads
Real number (ℝ)

High correlation  Zeros 

Distinct17
Distinct (%)5.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.508591
Minimum0
Maximum47
Zeros79
Zeros (%)27.1%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2025-02-17T11:41:37.958028image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median20
Q332
95-th percentile45
Maximum47
Range47
Interquartile range (IQR)32

Descriptive statistics

Standard deviation15.67677
Coefficient of variation (CV)0.76440017
Kurtosis-1.2869063
Mean20.508591
Median Absolute Deviation (MAD)15
Skewness0.015402403
Sum5968
Variance245.76113
MonotonicityNot monotonic
2025-02-17T11:41:38.011088image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 79
27.1%
30 23
 
7.9%
15 20
 
6.9%
37 18
 
6.2%
32 16
 
5.5%
22 16
 
5.5%
20 15
 
5.2%
42 12
 
4.1%
40 12
 
4.1%
17 12
 
4.1%
Other values (7) 68
23.4%
ValueCountFrequency (%)
0 79
27.1%
10 11
 
3.8%
12 9
 
3.1%
15 20
 
6.9%
17 12
 
4.1%
20 15
 
5.2%
22 16
 
5.5%
25 10
 
3.4%
27 10
 
3.4%
30 23
 
7.9%
ValueCountFrequency (%)
47 11
3.8%
45 11
3.8%
42 12
4.1%
40 12
4.1%
37 18
6.2%
35 6
 
2.1%
32 16
5.5%
30 23
7.9%
27 10
3.4%
25 10
3.4%

operating_hours
Real number (ℝ)

High correlation  Zeros 

Distinct8
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.1237113
Minimum0
Maximum30
Zeros54
Zeros (%)18.6%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2025-02-17T11:41:38.052997image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q34
95-th percentile6
Maximum30
Range30
Interquartile range (IQR)3

Descriptive statistics

Standard deviation4.0512009
Coefficient of variation (CV)1.2969191
Kurtosis31.171272
Mean3.1237113
Median Absolute Deviation (MAD)2
Skewness5.0008477
Sum909
Variance16.412229
MonotonicityNot monotonic
2025-02-17T11:41:38.119894image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 54
18.6%
1 45
15.5%
4 45
15.5%
2 42
14.4%
5 42
14.4%
3 36
12.4%
6 22
7.6%
30 5
 
1.7%
ValueCountFrequency (%)
0 54
18.6%
1 45
15.5%
2 42
14.4%
3 36
12.4%
4 45
15.5%
5 42
14.4%
6 22
7.6%
30 5
 
1.7%
ValueCountFrequency (%)
30 5
 
1.7%
6 22
7.6%
5 42
14.4%
4 45
15.5%
3 36
12.4%
2 42
14.4%
1 45
15.5%
0 54
18.6%

idle_hours
Categorical

High correlation 

Distinct3
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size14.3 KiB
0
177 
1
109 
34
 
5

Length

Max length2
Median length1
Mean length1.0171821
Min length1

Characters and Unicode

Total characters296
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 177
60.8%
1 109
37.5%
34 5
 
1.7%

Length

2025-02-17T11:41:38.202896image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-17T11:41:38.289043image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 177
60.8%
1 109
37.5%
34 5
 
1.7%

Most occurring characters

ValueCountFrequency (%)
0 177
59.8%
1 109
36.8%
3 5
 
1.7%
4 5
 
1.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 296
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 177
59.8%
1 109
36.8%
3 5
 
1.7%
4 5
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 296
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 177
59.8%
1 109
36.8%
3 5
 
1.7%
4 5
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 296
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 177
59.8%
1 109
36.8%
3 5
 
1.7%
4 5
 
1.7%

engine_hours
Real number (ℝ)

High correlation  Zeros 

Distinct6
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4501718
Minimum0
Maximum50
Zeros99
Zeros (%)34.0%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2025-02-17T11:41:38.365477image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q33
95-th percentile4
Maximum50
Range50
Interquartile range (IQR)3

Descriptive statistics

Standard deviation6.4524862
Coefficient of variation (CV)2.6334831
Kurtosis48.885893
Mean2.4501718
Median Absolute Deviation (MAD)1
Skewness6.9282939
Sum713
Variance41.634578
MonotonicityNot monotonic
2025-02-17T11:41:38.426814image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 99
34.0%
3 87
29.9%
1 42
14.4%
2 36
 
12.4%
4 22
 
7.6%
50 5
 
1.7%
ValueCountFrequency (%)
0 99
34.0%
1 42
14.4%
2 36
 
12.4%
3 87
29.9%
4 22
 
7.6%
50 5
 
1.7%
ValueCountFrequency (%)
50 5
 
1.7%
4 22
 
7.6%
3 87
29.9%
2 36
 
12.4%
1 42
14.4%
0 99
34.0%

transmission_hours
Real number (ℝ)

High correlation  Zeros 

Distinct6
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0206186
Minimum0
Maximum25
Zeros99
Zeros (%)34.0%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2025-02-17T11:41:38.490096image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q33
95-th percentile4
Maximum25
Range25
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.3517244
Coefficient of variation (CV)1.6587615
Kurtosis35.917649
Mean2.0206186
Median Absolute Deviation (MAD)1
Skewness5.5385428
Sum588
Variance11.234056
MonotonicityNot monotonic
2025-02-17T11:41:38.557345image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 99
34.0%
3 87
29.9%
1 42
14.4%
2 36
 
12.4%
4 22
 
7.6%
25 5
 
1.7%
ValueCountFrequency (%)
0 99
34.0%
1 42
14.4%
2 36
 
12.4%
3 87
29.9%
4 22
 
7.6%
25 5
 
1.7%
ValueCountFrequency (%)
25 5
 
1.7%
4 22
 
7.6%
3 87
29.9%
2 36
 
12.4%
1 42
14.4%
0 99
34.0%

mine
Categorical

Constant 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size14.3 KiB
1
291 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters291
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 291
100.0%

Length

2025-02-17T11:41:38.792397image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-17T11:41:38.856524image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 291
100.0%

Most occurring characters

ValueCountFrequency (%)
1 291
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 291
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 291
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 291
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 291
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 291
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 291
100.0%

machine
Categorical

Constant 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size14.3 KiB
1
291 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters291
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 291
100.0%

Length

2025-02-17T11:41:38.913530image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-17T11:41:38.969522image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 291
100.0%

Most occurring characters

ValueCountFrequency (%)
1 291
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 291
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 291
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 291
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 291
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 291
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 291
100.0%

operator
Categorical

Constant 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size14.3 KiB
1
291 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters291
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 291
100.0%

Length

2025-02-17T11:41:39.044950image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-17T11:41:39.109456image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 291
100.0%

Most occurring characters

ValueCountFrequency (%)
1 291
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 291
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 291
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 291
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 291
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 291
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 291
100.0%

Interactions

2025-02-17T11:41:35.431331image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T11:41:30.287067image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T11:41:30.951752image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T11:41:31.417104image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T11:41:32.326807image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T11:41:33.326272image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T11:41:34.077712image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T11:41:34.858784image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T11:41:35.483511image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T11:41:30.371359image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T11:41:31.005736image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T11:41:31.523614image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T11:41:32.404790image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T11:41:33.399695image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T11:41:34.180707image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T11:41:34.919774image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T11:41:35.533931image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T11:41:30.439173image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T11:41:31.046725image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T11:41:31.609595image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T11:41:32.489738image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T11:41:33.450698image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T11:41:34.326383image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T11:41:34.973116image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T11:41:35.620474image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T11:41:30.544819image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T11:41:31.126542image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T11:41:31.716625image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T11:41:32.912229image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T11:41:33.690722image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T11:41:34.566351image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T11:41:35.138964image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T11:41:35.674477image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T11:41:30.605806image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T11:41:31.185250image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T11:41:31.842803image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T11:41:33.014394image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T11:41:33.782529image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T11:41:34.618343image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T11:41:35.195929image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T11:41:35.755017image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T11:41:30.658805image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T11:41:31.228141image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T11:41:31.924292image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T11:41:33.108638image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T11:41:33.885381image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T11:41:34.676761image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T11:41:35.244212image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T11:41:35.995435image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T11:41:30.757165image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T11:41:31.277507image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T11:41:32.033278image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T11:41:33.199823image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T11:41:33.959714image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T11:41:34.732755image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T11:41:35.314435image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T11:41:36.071521image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T11:41:30.895269image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T11:41:31.326504image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T11:41:32.233168image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T11:41:33.263868image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T11:41:34.024724image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T11:41:34.803664image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-17T11:41:35.377952image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-02-17T11:41:39.170457image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
distance_travelledengine_hoursfuel_consumedididle_hoursno_of_loadsoperating_hoursshift_numbertonnagetransmission_hours
distance_travelled1.0000.9650.9890.0170.9980.9620.9910.0000.9300.965
engine_hours0.9651.0000.9560.0570.9980.9440.9740.0000.9101.000
fuel_consumed0.9890.9561.0000.0270.8810.9510.9820.0000.9170.956
id0.0170.0570.0271.0000.4620.0140.0270.0000.0030.057
idle_hours0.9980.9980.8810.4621.0000.7381.0000.0210.9461.000
no_of_loads0.9620.9440.9510.0140.7381.0000.9540.0000.9890.944
operating_hours0.9910.9740.9820.0271.0000.9541.0000.0210.9210.974
shift_number0.0000.0000.0000.0000.0210.0000.0211.0000.0000.021
tonnage0.9300.9100.9170.0030.9460.9890.9210.0001.0000.910
transmission_hours0.9651.0000.9560.0571.0000.9440.9740.0210.9101.000

Missing values

2025-02-17T11:41:36.328413image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-02-17T11:41:36.413661image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

idshift_numberdatedistance_travelledtonnagefuel_consumedno_of_loadsoperating_hoursidle_hoursengine_hourstransmission_hoursminemachineoperator
0122024-01-0157245121000111
1212024-01-02812064202011111
2322024-01-0219282152476144111
3412024-01-0319282190476144111
4522024-01-03201600000111
5612024-01-0412210120304133111
6722024-01-041221096304133111
7812024-01-0557245121000111
8922024-01-0516280160405133111
91012024-01-0618270144456144111
idshift_numberdatedistance_travelledtonnagefuel_consumedno_of_loadsoperating_hoursidle_hoursengine_hourstransmission_hoursminemachineoperator
28175022024-12-07302401000111
28275112024-12-0713224104324133111
28375312024-12-0810150100253022111
28475422024-12-0815259150375133111
28575622024-12-0910900000111
28675712024-12-0900000000111
28775912024-12-1012180120304133111
28876022024-12-1014210112354133111
28976222024-12-1115222120375133111
29076312024-12-1116240128405133111